Abstract

Given the dynamic nature of the brain, there has always been a motivation to move beyond ‘static’ functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain’s dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.

中文翻译：

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交易工具：从 fMRI 数据估计随时间变化的连接模式

摘要

考虑到大脑的动态特性，一直有超越“静态”功能连接的动机，静态功能连接表征了长时间的功能交互。数据采集​​的进展和分析神经成像方法的进步现在使我们能够使用 fMRI 在宏观尺度 (mm) 评估整个大脑的动态功能连接 (dFC) 及其基于网络的模拟、动态功能网络连接。这导致了分析方法的快速增长，其中一些方法非常复杂，需要可能使研究人员和神经科学家望而却步的技术专长。同时，只有通过神经科学家和精神病学家等领域专家的研究，才能在理解大脑活力与大脑疾病之间的关系方面取得真正的进展。本文旨在为dFC的应用提供一个温和的介绍。我们首先解释什么是 dFC 以及它可以在什么情况下使用。接下来，我们回顾了捕获 dFC 的两大类分析方法。我们讨论了 dFC 分析中的注意事项和注意事项。最后，我们将引导读者通过一个可公开访问的工具箱来捕获 dFC 属性，并简要回顾使用此工具箱计算的一些动态指标。